library(tidyverse)
library(plotly)
library(p8105.datasets)
Let’s get a small dataset of Airbnb’s in NYC:
data("nyc_airbnb")
nyc_airbnb =
nyc_airbnb |>
mutate(stars = review_scores_location / 2) |>
select(borough = neighbourhood_group,
neighbourhood, stars, price, room_type, lat, long) |>
drop_na(stars) |>
filter(
borough == "Manhattan",
room_type == "Entire home/apt",
price %in% 100:500)
We can add the price and rating/stars by creating a string
text_label and it will show up when you hover over the
item.
nyc_airbnb |>
mutate(text_label = str_c("Price: $", price, "\nRating: ", stars)) |>
plot_ly(x = ~lat, y = ~long, color = ~price, text = ~text_label,
type = "scatter", mode = "markers", alpha = 0.8)
nyc_airbnb |>
mutate(neighbourhood = fct_reorder(neighbourhood, price)) |>
plot_ly(y = ~price, color = ~neighbourhood, type = "box", colors = "viridis")
nyc_airbnb |>
count(neighbourhood) |>
mutate(neighbourhood = fct_reorder(neighbourhood, n)) |>
plot_ly(x = ~neighbourhood, y = ~n, color = ~neighbourhood, type = "bar", colors = "viridis")
This exists, but Prof. Goldsmith doesn’t recommend using it. The quality of the graphic is not as good and the interactivity is slower.
ggp_scatter =
nyc_airbnb |>
ggplot(aes(x = lat, y = long, color = price)) +
geom_point(alpha = 0.5)
ggplotly(ggp_scatter)
You can start with a template or File –> New File –> R Markdown –> From Template –> Flex Dashboard.
My code for the dashboard is in the dashboard.Rmd file.